Heart Disease Dataset

Heart Disease Dataset

In this article, we analyze the Heart Disease Dataset from the UCI Machine Learning Repository.

Picture Source: harvard.edu

Attribute Information:

  1. Age
  2. Sex
    • 0: Female
    • 1: Male
  3. Chest Pain Type
    • 1: Typical Angina
    • 2: Atypical Angina
    • 3: Non-Anginal Pain
    • 4: Asymptomatic
  1. Serum Cholestoral (in mg/dl )
  2. FBS: Fasting Blood Sugar > 120 mg/dl
    • 0 = False
    • 1 = True
  3. Resting Electrocardiographic Results
    • 0: normal
    • 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)
    • 2: showing probable or definite left ventricular hypertrophy by Estes' criteria
  4. Maximum Heart Rate Achieved
  5. Exercise Induced Angina
    • 0: No
    • 1: Yes
  6. Oldpeak = St Depression Induced By Exercise Relative To Rest
  7. Slope: The Slope Of The Peak Exercise ST Segment
    • 1: Upsloping
    • 2: Flat
    • 3: Downsloping
  8. Number Of Major Vessels (0-3) Colored By Flourosopy
  9. Thal
    • 3: Normal
    • 6: Fixed Defect
    • 7: Reversable Defect

Variable to be predicted

Problem Description

The object of the exercise is to develop a predictive model that can predict whether heart disease is present or absent based on the rest of the given features.

Exploratory Data Analysis

Feature Correlation

Age Distribution and Heart Disease

Resting Blood Pressure Distribution and Heart Disease

Serum Cholestoral Distribution and Heart Disease

Maximum Heart Rate Achieved and Heart Disease

In this section, we demonstrate the relationship between the maximum heart rate achieved and heart disease.


References

  1. Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., Schmid, J.J., Sandhu, S., Guppy, K.H., Lee, S. and Froelicher, V., 1989. International application of a new probability algorithm for the diagnosis of coronary artery disease. The American journal of cardiology, 64(5), pp.304-310.

  2. Aha, D. and Kibler, D., 1988. Instance-based prediction of heart-disease presence with the Cleveland database. University of California, 3(1), pp.3-2.

  3. Gennari, J.H., Langley, P. and Fisher, D., 1989. Models of incremental concept formation. Artificial intelligence, 40(1-3), pp.11-61.